For CTOs and VPs of engineering

Enterprise delivery with SSO, audit, and a clear record

Controls without leaving the workspace your engineers already ship from.

SSO via your IdP
Secrets stay off shared links
Signed build history

SSO

Audit receipt

Signed history of what ran

Regions

Deploy into accounts you own

Enterprise run evidence

Receipts

Checks

Unit suite
Integration
Deploy health

Cited files

app/api/checkout.ts
lib/billing/invoice.py
tests/checkout.spec.ts

Blast radius

3 services · 11 files · 0 shared schemas

Audit trail, checks, and staging on one forwardable link.

The category

Cost is the search keyword. What you need is controlled delivery: credentials stay in your accounts; every run keeps plan, preview, cost, and checks in one place security can review.

Identity stays in your IdP; workspace access follows your directory.
Signed build history on each run
Every run keeps plan, preview, cost, and checks on one shareable link.
Secrets never ride on share links; credentials stay in your accounts.

Controls

Enterprise controls without a second toolchain

Security review should not mean a second product. SSO, audit, and deploy topology live in the same workspace your engineers already use to ship, so IT can approve without slowing delivery.

Identity stays in your IdP; workspace access follows your directory.
Every run keeps plan, preview, cost, and checks on one shareable link.
Secrets never ride on share links; credentials stay in your accounts.
Regions and private deploy targets stay explicit on the topology, not buried in a ticket.

Talk to an engineer when you want controls that still ship.

SSO and access

Connect your IdP once. Roles and workspace access follow the directory you already trust. No parallel permission model for AI tooling.

SSO via your IdP
Directory-aligned roles

SSO via your IdP

Audit on the run

Who ran what, with which model, brief, and checks, attached to the project. Security gets a record; engineering keeps shipping.

Signed build history
Checks and cites on one link

Audit-ready receipt

Receipts

Checks

Unit suite
Integration
Deploy health

Cited files

app/api/checkout.ts
lib/billing/invoice.py
tests/checkout.spec.ts

Blast radius

3 services · 11 files · 0 shared schemas

Checks green. Staging URL live. Spend recorded on the same thread.

Regions and private deploy

Host and data residency choices stay visible on the deploy topology. You pick the target; Arvad keeps the trail on the same run.

Explicit region targets
Private deploy without a side project

The gap

Hidden costs draining your engineering budget

Third-party research on debt, maintenance spend, hiring, and burnout.

$1.52T

Accumulated Technical Debt (US Alone)

CISQ estimates the total cost of poor software quality at $2.41 trillion, with $1.52 trillion in accumulated technical debt. Developers spend 42% of their time on debt and maintenance instead of building new features.

CISQ, 2022

70%

IT Budgets Spent on Maintenance

Research consistently shows roughly 70% of IT budgets go to operations and maintenance, leaving only 30% for innovation. McKinsey finds 10–20% of new-product budgets are diverted to resolving debt-related issues.

McKinsey & Forrester

Developer Shortage by 2025

IDC projects a 4-million-developer global shortfall, generating $5.5 trillion in economic losses by 2026. Technical roles take 62–66 days to fill, and replacing a developer costs 100–150% of their annual salary.

IDC & SHRM

83%

Developer Burnout Rate

Haystack Analytics found 83% of developers suffer from burnout, driven by high workload (47%) and inefficient processes (31%). Burnout fuels an 18.3% tech industry turnover rate, compounding the talent crisis.

Haystack Analytics

Third-party data on AI-assisted development

Figures below cite McKinsey, Gartner, Forrester, GitHub/Microsoft, and IDC. They are industry research, not Arvad SLAs.

55.8%

Faster Task Completion

Developers completed tasks 55.8% faster with AI assistance in a controlled GitHub/Microsoft Research experiment.

GitHub/Microsoft RCT, 2023

20–45%

Cost Reduction on Engineering

Generative AI could automate 20–45% of current spending on software engineering functions.

McKinsey, 2023

$3.70

Return per $1 Invested in AI

Organizations average $3.70 return for every $1 invested in AI, with the top 5% achieving $10 per $1.

IDC AI Opportunity Study, 2024

90%

Enterprise Engineers Using AI by 2028

Gartner predicts 90% of enterprise software engineers will use AI code assistants by 2028, up from <14% in early 2024.

Gartner, 2024